Job TitleLead Data scientist Job DescriptionJob title Lead Data scientist Your role The Lead Data Scientist architects builds and runs productiongrade Machine Learning and Generative AI systemsowning the full lifecycle from model development to scalable cloud deployment and ongoing performance monitoring In addition the role partners with commercial stakeholders translate market customer data into decisionready insights and AIenabled analytics solutions that drive measurable outcomesOperating with a builder and translator mindset the individual rapidly develops MVP analytics solutions leverages AI to accelerate insight generation and ensures robust product engineering fundamentals data quality and governance The role plays a critical part in establishing a single source of truth for performance management across markets and channels while elevating analytics maturity from descriptive reporting to predictive and insightled decision making Key Responsibilities1 ML Deep Learning Model DevelopmentDesign train and optimize ML models for prediction classification ranking timeseries forecasting anomaly detection NLP and recommendation use cases Build robust experimentation workflows train validation strategy ablations error analysis and improve model quality through iterative tuning Ensure reproducibility and maintainability through clean code practices versioning and automated testing 2 GenAI Engineering LLMs RAG MCP finetuning Agents Build enterprisegrade LLM applications using RAG retrievalaugmented generation MCP and finetuning approaches chunking strategies embedding generation hybrid retrieval reranking prompt templates and citation attribution patterns Develop LLM applications with tool use function calling patterns and agentic workflows where appropriate Implement systematic evaluation curated eval sets prompt regression tests hallucination checks retrieval quality metrics and automated quality gates 3 ML LLM Operations Productionization Deployment MonitoringDeploy and operate realtime and batch inference solutions on Azure using managed endpoints and or containerized serving Build CI CD for ML systems automated packaging container builds model validation tests staged rollouts and rollback strategies Establish lifecycle management model registry versioning lineage promotion workflows and release governance Implement observability latency throughput cost drift signals data quality checks alerts and performance degradation monitoring 4 Pipeline Orchestration Automation Train Deploy Build standardized ML pipelines for training evaluation and deployment using orchestration tools cloudnative pipelines and or platform tools Automate dataset version management feature generation scheduled retraining triggers and approval workflows Define repeatable patterns for scalable experimentation and reliable production delivery 5 Analytics Products Dashboards Data GovernanceOwn key analytics outputs as products dashboards reusable datasets internal tools continuously improving them based on usage patterns and performance gaps Build and automate dashboards and analytical components using scalable SQL logic Python transformations and reusable modules Act as owner for critical commercial syndicated datasets e g GfK Circana Nielsen or equivalent definitions assumptions and limitations ensuring transparent logic and trust in outputs Partner with data engineering IT to ensure data quality harmonization and governance through strong validation and reconciliation practices 6 Stakeholder Partnership Decision Support Lightweight High Impact Serve as trusted analytics thought partner to senior stakeholders e g BU leadership Sales Marketing Finance shaping problem statements and aligning on success metrics Translate complex analytics into clear recommendations with a decisionoriented storyline sowhat nowwhat tailored for leadership forums and reviews Support performance reviews planning cycles and highpriority adhoc requests with speed rigor and confidence proactively challenge assumptions with factbased insights 7 Responsible AI Security and Risk Controls GenAIready Implement guardrails prompt injection defenses sensitive data protections output validation and secure tool execution patterns Apply responsible AI practices transparent evaluation criteria auditability and risk controls aligned to enterprise needs 8 Technical Leadership Leadlevel Expectations Set engineering standards for DS ML codebases design docs code review practices testing discipline and production readiness checklists Mentor data scientists ML engineers on modeling GenAI engineering and MLOps best practices Lead architectural decisions across modeling approaches retrieval stack serving patterns and evaluation strategy Core Skills CompetenciesMusthave Technical Strong Python

📌 Lead Data scientist (Bengaluru)
🏢 Philips
📍 Bengaluru